Giving AI "God Mode" access to our data is terrifying. How are you securing your AI agents? 🛑
Hey Hunters, 👋
Over the last month, my co-founder and I ran a #30DaysOfTrust challenge, diving deep into a massive problem we’ve noticed as AI adoption explodes: We are all building incredible AI agents, but handing them unrestricted "God Mode" access to our personal and enterprise data is a privacy disaster waiting to happen.
At SecuriX, our entire mission boils down to one simple imperative: Consume AI Responsibly.
Tomorrow, we are officially launching the first piece of our puzzle right here on Product Hunt: SecuriX Personal. It allows anyone to generate a unique MCP (Model Context Protocol) URL to securely connect their data to tools like Claude, ChatGPT, or VS Code—complete with a full audit trail and an instant kill switch also other use case specific policies for each provider.
But SecuriX Personal is just step one. Our ultimate vision is to build a robust Agent Access Security Broker (AASB)—a B2B developer-facing infrastructure API layer so teams don't have to build security infrastructure from scratch. We are actively building the Developer and Enterprise tiers right now to handle Policy-as-Code (OPA/Rego) and Draft-Only policies.
Since we are building in public and launching tomorrow, I wanted to tap into the brains of the builders in this community:
The Fear Factor: As makers, what is your biggest hesitation or security nightmare when hooking up LLMs to your databases or APIs?
Your Stack: What AI models or MCP-supporting apps are currently dominating your workflow?
The Developer Wishlist: For the devs building multi-agent systems—what specific data providers, custom tools, or policy integrations would make your life easier?
Would love to hear how you are all handling data privacy in your AI builds. I'll be hanging out in the comments all day—let's talk architecture! 👇
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